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Plaid’s Data organization is building models, services and platforms that improve how millions of users understand and grow their financial lives. We are looking for a tech lead manager with experience applying state-of-the-art machine learning and data science techniques in the CGX/EWA/NEA domain. We value not only technical know-how, but also creativity, user empathy, and teamwork. You’ll be a Tech Lead Manager overseeing a team of Machine Learning Engineers and Data Scientists embedded across Plaid’s CGX, EWA, and NEA product areas. Your team builds and scales ML systems that power growth personalization, financial forecasting, and network insights—driving measurable impact across Plaid’s consumer experiences.
Job Responsibility:
Leading a talented ML team that builds systems powering growth, personalization, and financial insights across CGX, EWA, and NEA
Working hands-on in ML model development and productionization, driving business impact
Partnering with product and platform teams to turn data and models into production features that reach millions of users
Shaping Plaid’s broader ML ecosystem by influencing technical standards, shared pipelines, and model development best practices
Growing as a leader who balances deep technical engagement with long-term organizational and strategic impact
Requirements:
7+ years of industry experience developing machine learning models from inception to business impact
Proven ability to tailor your solutions to business problems in a cross functional team
3+ years of experience in DS/ML managerial role
Deep understanding of modern machine learning techniques and their mathematical models, such as classification, clustering, optimization, deep neural network and natural language processing
Strong product intuition and excitement to work fast and iteratively
Proven track record of strategic influence by partnering with key stakeholders to shape and launch cutting-edge ML-driven products that drive revenue and market growth
Bachelor's degree or equivalent work experience in Computer Science, Mathematics, Engineering, Economics, or a closely related field
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